Ms. Nathalia Hidalgo Leite | Artificial Neural Networks | Best Researcher Award

Ms. Nathalia Hidalgo Leite | Artificial Neural Networks | Best Researcher Award

Ms. Nathalia Hidalgo Leite, State University of Campinas, Brazil

๐ŸŽ“ Nathalia Hidalgo Leite is a Ph.D. candidate in Energy Systems Planning at the State University of Campinas (Unicamp) ๐Ÿ‡ง๐Ÿ‡ท, focusing on electric mobility. She also holds an MBA in Value Investing from UniBTA and an M.S. in Energy Systems Planning from Unicamp, and a B.S. in Agronomic Engineering from UFSCar ๐ŸŒฑ. As a researcher at CPTEn and CPFL Energy, and an instructor at Unicamp, she has contributed significantly to energy systems planning and education. Her international academic experience includes programs in Portugal ๐Ÿ‡ต๐Ÿ‡น, Denmark ๐Ÿ‡ฉ๐Ÿ‡ฐ, Spain ๐Ÿ‡ช๐Ÿ‡ธ, Italy ๐Ÿ‡ฎ๐Ÿ‡น, and the USA ๐Ÿ‡บ๐Ÿ‡ธ. Nathalia’s research interests span the economic and financial viability of energy systems, artificial neural networks, and renewable energy integration ๐ŸŒž. Proficient in Portuguese, English, and Spanish, she holds numerous certifications in finance and technical skills ๐Ÿ“Š. Her achievements are recognized by multiple academic and extracurricular awards, underscoring her dedication and multifaceted talents ๐ŸŒŸ.

๐ŸŒ Professional Profile:

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๐ŸŽ“ Educational Background

Nathalia Hidalgo Leite is currently pursuing a Ph.D. in Energy Systems Planning at the State University of Campinas (Unicamp) ๐Ÿ‡ง๐Ÿ‡ท, focusing on the economic and financial viability for electric mobility. She also holds an MBA in Value Investing from UniBTA and an M.S. in Energy Systems Planning from Unicamp, where she studied the economic viability of photovoltaic solar energy. Nathalia completed her B.S. in Agronomic Engineering at the Federal University of Sao Carlos (UFSCar), researching artificial neural networks applied to Asian soybean rust ๐ŸŒฑ.

๐Ÿข Professional Experience

Nathalia is an accomplished researcher and instructor. At Unicamp, she has taught courses in computer algorithms, programming, numerical calculation, and discrete mathematics. As a researcher at the Sao Paulo Center for Energy Transition Studies (CPTEn) and previously at CPFL Energy, she has made significant contributions to energy systems planning. Nathalia also worked as a Financial Planning and Analysis Specialist at Grupo JLJ and interned at Ecomark Indรบstria e Comรฉrcio de Fertilizantes Especiais Ltda ๐ŸŒŸ.

๐ŸŒ International Experience

Nathalia has enriched her academic journey with several exchange programs. She spent six months at the University of Lisbon ๐Ÿ‡ต๐Ÿ‡น and the University of Southern Denmark ๐Ÿ‡ฉ๐Ÿ‡ฐ, three months at Universitat Politรจcnica de Valรจncia ๐Ÿ‡ช๐Ÿ‡ธ, one month at Universitร  degli Studi di Roma La Sapienza ๐Ÿ‡ฎ๐Ÿ‡น, and also attended Beverly Hills High School ๐Ÿ‡บ๐Ÿ‡ธ and Moore Elementary School in Colorado ๐Ÿ‡บ๐Ÿ‡ธ during her earlier education ๐Ÿ“š.

๐Ÿ“ Research Interests

Nathalia’s research interests include the economic and financial viability of energy systems, artificial neural networks, and the integration of renewable energy sources. She is particularly focused on interdisciplinary approaches to solving complex problems in energy planning and sustainability ๐ŸŒž.

๐ŸŒ Certifications and Skills

Nathalia holds numerous certifications in finance, photovoltaic systems, scientific writing, and programming. She is proficient in Portuguese (native), English (fluent), and Spanish (advanced). Her technical skills and continuous learning make her a versatile and knowledgeable professional in her field ๐Ÿ“Š.

๐Ÿ† Awards and Recognitions

Nathalia has received several awards for academic and extracurricular excellence, including the Excellence in Academic Performance award from Anglo Middle School ๐Ÿ‡ง๐Ÿ‡ท and multiple accolades from Lincoln Junior High School ๐Ÿ‡บ๐Ÿ‡ธ for academic achievement, athletic performance, and leadership. These recognitions highlight her dedication and multifaceted talents ๐ŸŒŸ.

Publication Top Notes:

1. ย Artificial Neural Networks Applied to Plant Disease
2. ย Study of Asian Soybean Rust

 

 

Prof Dr. Vaneet Aggarwal | Machine Learning | Best Researcher Award

Prof Dr. Vaneet Aggarwal | Machine Learning | Best Researcher Award

Prof Dr. Vaneet Aggarwal, Purdue University, United States

Prof. Dr. Vaneet Aggarwal, an accomplished scholar with a Ph.D. in Electrical Engineering from Princeton University, is currently a distinguished faculty member at Purdue University. With a diverse academic background spanning machine learning, computational perception, and computer science, he has garnered recognition for his impactful contributions. ๐ŸŒŸ His research interests encompass a wide array of cutting-edge fields, including reinforcement learning, generative AI, quantum machine learning, and federated learning. ๐Ÿง  Through leadership roles in prestigious journals and institutions, such as ACM Journal of Transportation Systems and Purdue-TVS Advanced AI Program, he continues to drive innovation at the intersection of AI and various domains, ranging from networking to healthcare. ๐Ÿš€ Honored with accolades like the IEEE Communications Society William R. Bennett Prize and featured in esteemed publications like Nature and Axios News, Prof. Aggarwal’s work exemplifies excellence in advancing the frontiers of artificial intelligence. ๐Ÿ†

๐ŸŒ Professional Profile:

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Education

  • Ph.D. in Electrical Engineering, Princeton University, Princeton, New Jersey, July 2010
    • Thesis: Decisions in Distributed Wireless Networks with Imprecise Information
    • Minors: Machine Learning and Computational Perception, Computer Science
    • Advisor: Prof. A. Robert Calderbank
  • M.A. in Electrical Engineering, Princeton University, Princeton, New Jersey, June 2007
  • Bachelor of Technology in Electrical Engineering, Indian Institute of Technology, Kanpur, May 2005

Work Experience

  • Purdue University, West Lafayette, Jan. 2015 – Current: Faculty in the School of Industrial Engineering and Elmore Family School of Electrical and Computer Engineering
  • KAUST, Saudi Arabia, May 2022 – Aug 2023: Visiting Professor
  • IIIT Delhi, Jan 2022 – Mar 2023: Adjunct Professor
  • Plaksha University, Nov 2022 – Jan 2023: Adjunct Professor
  • Indian Institute of Science (IISc) Bangalore, May 2018 – Apr 2019: VAJRA Adjunct Faculty
  • AT&T Labs Research, NJ, Aug. 2010 – Dec. 2014: Senior Member of Technical Staff-Research
  • Columbia University, New York, NY, Aug. 2013 – Dec. 2014: Adjunct Assistant Professor

Key Leadership Experience

  • ACM Journal of Transportation Systems, co-Editor-in-Chief, 2022-Current
  • Director of Potential NSF AI Institute on Human-AI Decision Making at Scale, 2021-Aug 2022
  • Founding Technical Lead Purdue-TVS Advanced AI Program, 2021-Current
  • AI Thrust Lead in Purdue Center of Intelligent Infrastructures, 2019-Current

Honors & Awards

  • Purdue University Faculty Scholar Professor, 2024-Current
  • IEEE ComSoc Distinguished Lecturer for the class of 2024-2025
  • 2024 IEEE Communications Society William R. Bennett Prize
  • Featured on Axios News for paper [J176] in 2023
  • Featured on Cover of Nature for paper [J139] in 2023
  • NeurIPS Workshop Best Paper Award in 2021
  • Most Impactful Faculty Innovator, Purdue University in 2020
  • Infocom Workshop Best Paper Award in 2018

Research Interests:

Reinforcement Learning; Generative AI; Quantum Machine Learning; Federated Learning; Applications of ML in Networking, Transportation, Robotics, Manufacturing, Healthcare, and Biomedical.

Publication Top Notes:

  1. Title: Design and characterization of a full-duplex multiantenna system for WiFi networks
    • Journal: IEEE Transactions on Vehicular Technology
    • Citations: 665
    • Year: 2013
  2. Title: Efficient low rank tensor ring completion
    • Proceedings: Proceedings of the IEEE International Conference on Computer Vision
    • Citations: 192
    • Year: 2017
  3. Title: Wide compression: Tensor ring nets
    • Proceedings: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
    • Citations: 180
    • Year: 2018
  4. Title: Prometheus: Toward quality-of-experience estimation for mobile apps from passive network measurements
    • Proceedings: Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
    • Citations: 177
    • Year: 2014
  5. Title: Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
    • Journal: IEEE Transactions on Intelligent Transportation Systems
    • Citations: 159
    • Year: 2019